Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

# Exercise 
This third exercise will use gradient boosting to predict the Advertising dataset's outcome to compare the results with the two previous algorithms.

Readers of _Machine Learning for Absolute Beginners Second Edition_ will be familiar with the regression variant of gradient boosting. We will instead use the classification version of the algorithm in this exercise, which predicts a discrete variable and comes with slightly different hyperparameters.

# 1) Import libraries
This model uses the classification form of Gradient Boosting from Scikit-learn’s ensemble package.




Let's implement gradient boosting for the classifier problem.

Implementation of Gradient Boosting Classifier

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Conclusion

Appendix

Implementation of Gradient Boosting Classifier

Exercise